[JAX] Support custom recipe and custom collection name when creating quantizer sets (#2059)

* Support setting collection name for quantizer set Flax variables in TransformerEngineBase flax module
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Support creating quantizer set from a recipe directly
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Fix debug error format string in gemm.py
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

* Lint
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

---------
Signed-off-by: Jeremy Berchtold <jberchtold@nvidia.com>

transformer_engine/jax/cpp_extensions/gemm.py

@@ -1090,7 +1090,7 @@ def _jax_gemm(
         if lhs.scaling_mode == ScalingMode.MXFP8_1D_SCALING:
             return _jax_gemm_mxfp8_1d(lhs, rhs, dim_nums)
 
-        raise NotImplementedError("Unsupported ScalingMode: {lhs.scaling_mode}")
+        raise NotImplementedError(f"Unsupported ScalingMode: {lhs.scaling_mode}")
 
     lhs_q, rhs_q = _quantize_gemm_operands(lhs, rhs, lhs_quantizer, rhs_quantizer, contracting_dims)
 

transformer_engine/jax/flax/module.py

@@ -337,21 +337,28 @@ class TransformerEngineBase(nn.Module):  # pylint: disable=too-few-public-method
     Base class of transformer engine
     """
 
-    def generate_quantizer_set(self, postfix: str = ""):
+    def generate_quantizer_set(
+        self, postfix: str = "", variable_collection: str = None, fp8_recipe=None
+    ):
         """
         Generate a set of FP8 meta for a GEMM.
         """
 
         def generate_quantize_meta(quantizer_name: str):
+            collection_name = (
+                variable_collection
+                if variable_collection is not None
+                else QuantizeConfig.COLLECTION_NAME
+            )
             scale = self.variable(
-                QuantizeConfig.COLLECTION_NAME,
+                collection_name,
                 f"{quantizer_name}{postfix}_scale",
                 jnp.ones,
                 (1,),
                 jnp.float32,
             ).value
             amax_history = self.variable(
-                QuantizeConfig.COLLECTION_NAME,
+                collection_name,
                 f"{quantizer_name}{postfix}_amax_history",
                 jnp.zeros,
                 (QuantizeConfig.AMAX_HISTORY_LEN,),
@@ -368,7 +375,7 @@ class TransformerEngineBase(nn.Module):  # pylint: disable=too-few-public-method
         else:
             kwargs = {}
 
-        quantizer_set = QuantizerFactory.create_set(**kwargs)
+        quantizer_set = QuantizerFactory.create_set(fp8_recipe=fp8_recipe, **kwargs)
         return quantizer_set
 
 

transformer_engine/jax/quantize/quantizer.py

@@ -16,12 +16,14 @@ import jax
 import jax.numpy as jnp
 from jax.tree_util import register_pytree_node_class
 from transformer_engine_jax import QuantizeLayout
+from transformer_engine.common import recipe
 
 from .scaling_modes import ScalingMode
 from .tensor import ScaledTensor, ScaledTensor1x, ScaledTensor2x, ScaledTensorFactory
 from .helper import (
     QuantizeConfig,
     AmaxComputeAlgo,
+    _get_scaling_mode,
 )
 from .device_utils import is_fp8_gemm_with_all_layouts_supported
 
@@ -878,11 +880,12 @@ class QuantizerFactory:
     @staticmethod
     def create_set(
         n_quantizer_sets: int = 1,
-        scaling_mode: ScalingMode = None,
+        scaling_mode: Optional[ScalingMode] = None,
         fwd_dtype: jnp.dtype = None,
         bwd_dtype: jnp.dtype = None,
         is_2x2x: bool = None,
         n_groups: int = None,
+        fp8_recipe: Optional[recipe.Recipe] = None,
         **kwargs,
     ) -> tuple[Union[tuple[Quantizer], None]]:
         """Create one or more sets of quantizers.
@@ -894,12 +897,25 @@ class QuantizerFactory:
             bwd_dtype: Data type for backward pass, default is QuantizeConfig.BWD_DTYPE
             is_2x2x: Whether to use 2x2x quantization, default is QuantizeConfig.IF_QUANTIZE_2X
             n_groups:
+            fp8_recipe: Recipe to use for quantization. Scaling mode can be specified directly via the scaling_mode parameter or indirectly via recipe. Recipe is preferred as it will support additional recipes in future where scaling mode differs between x, kernel, and grad in the quantizer set.
             **kwargs: Additional arguments for quantizer initialization
 
         Returns:
             A single quantizer set or tuple of quantizer sets
         """
-        scaling_mode = scaling_mode or QuantizeConfig.SCALING_MODE
+
+        assert scaling_mode is None or fp8_recipe is None, (
+            "Cannot specify both scaling_mode and fp8_recipe when creating a quantizer set. Scaling"
+            " mode can be specified directly via the scaling_mode parameter or indirectly via"
+            " recipe. Recipe is preferred as it will support additional recipes in future where"
+            " scaling mode differs between x, kernel, and grad in the quantizer set."
+        )
+
+        if fp8_recipe is not None:
+            # TODO(jberchtold): once recipe and scaling mode are decoupled update this logic
+            scaling_mode = _get_scaling_mode(fp8_recipe)
+        else:
+            scaling_mode = scaling_mode or QuantizeConfig.SCALING_MODE
         fwd_dtype = fwd_dtype or QuantizeConfig.FWD_DTYPE
         bwd_dtype = bwd_dtype or QuantizeConfig.BWD_DTYPE
         if is_2x2x is None: